Cribiform and intraductal carcinoma in hereditary prostate cancer: clinical and pathological analysis of 20 cases.

Cribiform and intraductal carcinoma are patterns of aggressive prostate carcinoma. This study investigated the clinical and pathological features of hereditary prostate cancer. Twenty cases of hereditary prostate cancer from 11 family lines treated at the First Affiliated Hospital of Zhejiang University School of Medicine between 2016-2022 were included to summarize the clinical and pathological features by analyzing clinical information including follow up the survival of the patients and pathological features. Of the 20 hereditary prostate cancer cases, 19 were radical prostate specimens and 1 was a biopsy specimen. The mean age at diagnosis of the patients was 67.55 years and the mean PSA was 15.44 ng/ml, of which 10 cases had PSA ≥ 10 ng/ml and 5 cases had PSA ≥ 20 ng/ml. Of the 19 radical prostate specimens, Gleason cribriform pattern (Gleason grade 4) of PCa is observed in 15 cases (78.95%), and intraductal carcinoma, usually a rare form, is seen in 9 cases (47.3%). Two cases demonstrated pelvic lymph node metastasis, and 7 cases (35%) belonged to high-risk or very high-risk PCa. One case (5.26%) showed partial deletion of expression of RB1, and 13 cases (68.42%) showed deletion of expression of PTEN. Follow-up was 4-90 months, 2 cases had biochemical recurrence and 1 case died from prostate cancer. The mean age at diagnosis of this group of patients with hereditary prostate cancer was 67.55 years, the mean preoperative PSA was 15.44 ng/ml, and their histomorphology was characterized by a high percentage of intraductal carcinoma and cribriform pattern of the prostate.

A Method for Automatically Predicting the Radiation-Induced Vulnerability of Unit Integrated Circuits.

With the rapid development of semiconductor technology, the reduction in device operating voltage and threshold voltage has made integrated circuits more susceptible to the effects of particle radiation. Moreover, as process sizes decrease, the impact of charge sharing effects becomes increasingly severe, with soft errors caused by single event effects becoming one of the main causes of circuit failures. Therefore, the study of sensitivity evaluation methods for integrated circuits is of great significance for promoting the optimization of integrated circuit design, improving single event effect experimental methods, and enhancing the irradiation reliability of integrated circuits. In this paper, we first established a device model for the charge sharing effect and simulated it under reasonable conditions. Based on the simulation results, we then built a neural network model to predict the charge amounts in primary and secondary devices. We also propose a comprehensive automated method for calculating soft errors in unit circuits and validated it through TCAD simulations, achieving an error margin of 2.8-4.3%. This demonstrated the accuracy and effectiveness of the method we propose.

Predicting the cognitive function status in end-stage renal disease patients at a functional subnetwork scale.

Brain functional networks derived from functional magnetic resonance imaging (fMRI) provide a promising approach to understanding cognitive processes and predicting cognitive abilities. The topological attribute parameters of global networks are taken as the features from the overall perspective. It is constrained to comprehend the subtleties and variances of brain functional networks, which fell short of thoroughly examining the complex relationships and information transfer mechanisms among various regions. To address this issue, we proposed a framework to predict the cognitive function status in the patients with end-stage renal disease (ESRD) at a functional subnetwork scale (CFSFSS). The nodes from different network indicators were combined to form the functional subnetworks. The area under the curve (AUC) of the topological attribute parameters of functional subnetworks were extracted as features, which were selected by the minimal Redundancy Maximum Relevance (mRMR). The parameter combination with improved fitness was searched by the enhanced whale optimization algorithm (E-WOA), so as to optimize the parameters of support vector regression (SVR) and solve the global optimization problem of the predictive model. Experimental results indicated that CFSFSS achieved superior predictive performance compared to other methods, by which the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were up to 0.5951, 0.0281 and 0.9994, respectively. The functional subnetwork effectively identified the active brain regions associated with the cognitive function status, which offered more precise features. It not only helps to more accurately predict the cognitive function status, but also provides more references for clinical decision-making and intervention of cognitive impairment in ESRD patients.

Mushroom Poisoning Outbreaks - China, 2023.

What is already known about this topic?: Mushroom poisoning poses a significant food safety concern in China, with a total of 196 species identified in poisoning incidents by the end of 2022. What is added by this report?: In 2023, the China CDC conducted an investigation into 505 cases of mushroom poisoning spanning 24 provincial-level administrative divisions. This investigation resulted in 1,303 patients and 16 deaths, yielding a case fatality rate of 1.23%. A total of 97 mushrooms were identified as the cause of 6 distinct clinical disease types, with 12 species newly documented as poisonous mushrooms in China. What are the implications for public health practice?: Close collaboration among CDC staff, physicians, and mycologists remains crucial for the control and prevention of mushroom poisoning in the future.

Superelastic and superflexible cellulose aerogels for thermal insulation and oil/water separation.

Aerogels with low thermal conductivity and high adsorption capacity present a promising solution to curb water pollution caused by organic reagents as well as mitigate heat loss. Although aerogels exhibiting good adsorption capacity and thermal insulation have been reported, materials with mechanical integrity, high flexibility and shear resistance still pose a formidable task. Here, we produced bacterial cellulose-based ultralight multifunctional hybrid aerogels by using freeze-drying followed by chemical vapor deposition silylation method. The hybrid aerogels displayed a low density of 10-15 mg/cm3, high porosity exceeding 99.1 %, low thermal conductivity (27.3-29.2 mW/m.K) and superior hydrophobicity (water contact angle>120o). They also exhibited excellent mechanical properties including superelasticity, high flexibility and shear resistance. The hybrid aerogels demonstrated high heat shielding efficiency when used as an insulating material. As a selective oil absorbent, the hybrid aerogels exhibit a maximum adsorption capacity of up to approximately 156 times its own weight and excellent recoverability. Especially, the aerogel's highly accessible porous microstructure results in an impressive flux rate of up to 162 L/h.g when used as a filter in a continuous oil-water separator to isolate n-hexane-water mixtures. This work presents a novel endeavor to create high-performance, sustainable, reusable, and adaptable multifunctional aerogels.

Customized design and biomechanical property analysis of 3D-printed tantalum intervertebral cages.

BACKGROUND: Intervertebral cages used in clinical applications were often general products with standard specifications, which were challenging to match with the cervical vertebra and prone to cause stress shielding and subsidence. OBJECTIVE: To design and fabricate customized tantalum (Ta) intervertebral fusion cages that meets the biomechanical requirements of the cervical segment. METHODS: The lattice intervertebral cages were customized designed and fabricated by the selective laser melting. The joint and muscle forces of the cervical segment under different movements were analyzed using reverse dynamics method. The stress characteristics of cage, plate, screws and vertebral endplate were analyzed by finite element analysis. The fluid flow behaviors and permeability of three lattice structures were simulated by computational fluid dynamics. Compression tests were executed to investigate the biomechanical properties of the cages. RESULTS: Compared with the solid cages, the lattice-filled structures significantly reduced the stress of cages and anterior fixation system. In comparison to the octahedroid and quaddiametral lattice-filled cages, the bitriangle lattice-filled cage had a lower stress shielding rate, higher permeability, and superior subsidence resistance ability. CONCLUSION: The inverse dynamics simulation combined with finite element analysis is an effective method to investigate the biomechanical properties of the cervical vertebra during movements.

Food-Borne Poisoning Accident from Amanitin Toxin in Wild Mushrooms - Xingtai City, Hebei Province, China, 2023.

What is already known about this topic?: Fatal poisonings caused by wild mushrooms containing amanita toxins pose a significant threat in the southern regions of China. These toxins primarily induce gastrointestinal symptoms initially, which are then followed by potentially life-threatening acute liver damage. What is added by this report?: This report contributes to the existing knowledge on these cases of poisoning by documenting the second occurrences in Hebei Province and the first occurrences in Xingtai City. Five individuals reported consuming wild mushrooms from the same origin, and laboratory tests confirmed the presence of α-amanitin in their blood samples. What are the implications for public health practice?: This underscores the risk associated with the collection and consumption of amanita toxin-containing mushrooms in Hebei. It is important to note that the identification of toxic and non-toxic mushrooms should not solely rely on personal experience or appearance.

An Improved Model of Single-Event Transients Based on Effective Space Charge for Metal-Oxide-Semiconductor Field-Effect Transistor.

In this paper, a single-event transient model based on the effective space charge for MOSFETs is proposed. The physical process of deposited and moving charges is analyzed in detail. The influence of deposited charges on the electric field in the depletion region is investigated. The electric field decreases in a short time period due to the neutralization of the space charge. After that, the electric field increases first and then decreases when the deposited charge is moved out. The movement of the deposited charge in the body mainly occurs through ambipolar diffusion because of its high-density electrons and holes. The derivation of the variation in electric field in the depletion region is modeled in the physical process according to the analysis. In combination with the ambipolar diffusion model of excessive charge in the body, a physics-based model is built to describe the current pulse in the drain terminal. The proposed model takes into account the influence of multiple factors, like linear-energy transfer (LET), drain bias, and the doping concentration of the well. The model results are validated with the simulation results from TCAD. Through calculation, the root-mean-square error (RMSE) between the simulation and model is less than 3.7 × 10-4, which means that the model matches well with the TCAD results. Moreover, a CMOS inverter is simulated using TCAD and SPICE to validate the applicability of the proposed model in a circuit-level simulation. The proposed model captures the variation in net voltage in the inverter. The simulation result obviously shows the current plateau effect, while the relative error of the pulse width is 23.5%, much better than that in the classic model. In comparison with the classic model, the proposed model provides an RMSE of 7.59 × 10-5 for the output current curve and an RMSE of 0.158 for the output voltage curve, which are significantly better than those of the classic model. In the meantime, the proposed model does not produce extra simulation time compared with the classic double exponential model. So, the model has potential for application to flow estimation of the soft error rate (SER) at the circuit level to improve the accuracy of the results.

Overexpression of TPL2 may be a predictor of good prognosis in patients with breast invasive ductal carcinoma.

The objective of this study was to investigate the clinical significance and roles of tumor progression locus 2 (TPL2) and peptidyl-prolyl cis-trans isomerase 1 (Pin1) in the occurrence and development of breast invasive ductal carcinoma (IDC). Immunohistochemistry was used to detect the expression of TPL2 and Pin1 in human breast tissues, which included normal breast tissues (Normal), tissues with fibrocystic changes (FCC), ductal carcinoma in situ (DCIS), and IDC. The roles of TPL2 and Pin1 in the occurrence and development of IDC, as well as the correlation between their expression levels and clinicopathological parameters, were analyzed. Compared with Normal and FCC groups, the overexpression of TPL2 and Pin1 was significantly increased in DCIS and IDC groups (DCIS vs Normal: P = 0.002/P < 0.001; IDC vs Normal: P = 0.007/P = 0.003; DCIS vs. FCC: P = 0.008/P = 0.004; IDC vs. FCC: P = 0.04/P = 0.043). The expression levels of TPL2 and Pin1 were positively correlated in DCIS and IDC groups (P = 0.001, P = 0.011). In the IDC group, the Ki67 level in the TPL2 overexpression group was significantly lower than that in the TPL2 low expression group (P = 0.02). The TPL2 overexpression rate was significantly higher in IDC with histological grades 1-2 than that in IDC with histological grade 3 (P = 0.029). The TPL2 overexpression rate in IDC with tumor-node-metastasis (TNM) stage I was significantly higher than that in IDC with TNM stages II-III (P = 0.035). We conclude that TPL2 and Pin1 may synergistically promote the occurrence and development of IDC, but TPL2 overexpression may be an early molecular event in IDC development. TPL2 overexpression is significantly related with IDC with lower malignancy or earlier TNM stage, suggesting that the prognosis of IDC patients with TPL2 overexpression may be better and TPL2 overexpression may be a predictor of good prognosis in IDC.

A High-Reliability 12T SRAM Radiation-Hardened Cell for Aerospace Applications.

The static random-access memory (SRAM) cells used in the high radiation environment of aerospace have become highly vulnerable to single-event effects (SEE). Therefore, a 12T SRAM-hardened circuit (RHB-12T cell) for the soft error recovery is proposed using the radiation hardening design (RHBD) concept. To verify the performance of the RHB-12T, the proposed cell is simulated by the 28 nm CMOS process and compared with other hardened cells (Quatro-10T, WE-Quatro-12T, RHM-12T, RHD-12T, and RSP-14T). The simulation results show that the RHB-12T cell can recover not only from single-event upset caused by their sensitive nodes but also from single-event multi-node upset caused by their storage node pairs. The proposed cell exhibits 1.14×/1.23×/1.06× shorter read delay than Quatro-10T/WE-Quatro-12T/RSP-14T and 1.31×/1.11×/1.18×/1.37× shorter write delay than WE-Quatro-12T/RHM-12T/RHD-12T/RSP-14T. It also shows 1.35×/1.11×/1.04× higher read stability than Quatro-10T/RHM-12T/RHD-12T and 1.12×/1.04×/1.09× higher write ability than RHM-12T/RHD-12T/RSP-14T. All these improvements are achieved at the cost of a slightly larger area and power consumption.

Predicting band gaps of MOFs on small data by deep transfer learning with data augmentation strategies.

Porphyrin-based MOFs combine the unique photophysical and electrochemical properties of metalloporphyrins with the catalytic efficiency of MOF materials, making them an important candidate for light energy harvesting and conversion. However, accurate prediction of the band gap of porphyrin-based MOFs is hampered by their complex structure-function relationships. Although machine learning (ML) has performed well in predicting the properties of MOFs with large training datasets, such ML applications become challenging when the training data size of the materials is small. In this study, we first constructed a dataset of 202 porphyrin-based MOFs using DFT computations and increased the training data size using two data augmentation strategies. After that, four state-of-the-art neural network models were pre-trained with the recognized open-source database QMOF and fine-tuned with our augmented self-curated datasets. The GCN models predicted the band gaps of the porphyrin-based materials with the lowest RMSE of 0.2767 eV and MAE of 0.1463 eV. In addition, the data augmentation strategy rotation and mirroring effectively decreased the RMSE by 38.51% and MAE by 50.05%. This study demonstrates that, when proper transfer learning and data augmentation strategies are applied, machine learning models can predict the properties of MOFs using small training data.